CN112651323B - Chinese handwriting recognition method and system based on text line detection - Google Patents
Chinese handwriting recognition method and system based on text line detection Download PDFInfo
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Abstract
The invention provides a Chinese handwriting recognition method and system based on text line detection, which comprises the following steps: segmenting an image to be identified into text lines; identifying the information of the inserted characters according to the average height of the text lines; extracting the image characteristics of the text lines by utilizing a pre-constructed multilayer convolutional neural network model; extracting character sequence features from the image features by utilizing a pre-constructed bidirectional recurrent neural network model; and identifying the text content of the image to be identified according to the inserted character information and the character sequence characteristics. The invention can accurately identify the text content in the handwritten Chinese image and has high identification efficiency.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to a Chinese handwriting recognition method and system based on text line detection.
Background
Chinese recognition is an important issue in the field of computer vision and is one of the most challenging problems in the field of OCR. Chinese handwriting recognition and Chinese print recognition are the main research content of Chinese recognition. The print recognition is developed to date, has a high recognition rate and is widely applied. Chinese handwriting recognition can be subdivided into offline handwriting recognition and online handwriting recognition. The on-line handwriting recognition needs to write on a specific electronic device, the electronic device senses stroke tracks, the number of strokes and the writing speed when a user writes Chinese characters, and the machine processes writing information captured by an instrument in real time to recognize the Chinese characters. While offline Chinese handwriting recognition can only acquire images of Chinese characters by using image acquisition equipment and recognize characters by analyzing the images.
Because Chinese characters have various categories, complicated structures and more characters with similar shapes, the writing randomness is large, the normalization is lacked, and the writing style difference of different people is large; and because the influence of the Chinese character structure and the writing are not standard, the Chinese character handwriting recognition method is difficult to segment for the adhered characters, the inserted characters and the like, so that the accuracy of the off-line Chinese handwriting character recognition can not be well applied to the industry.
In recent years, with the development of deep learning and the improvement of hardware computing performance, the computer vision field has achieved good results in a plurality of fields of pattern recognition, so that the use of deep learning technology has great significance for solving the recognition of Chinese handwriting.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention provides a method and system for Chinese handwriting recognition based on text line detection, so as to solve the above-mentioned technical problems.
In a first aspect, the present invention provides a method for Chinese handwriting recognition based on text line detection, comprising:
segmenting an image to be identified into text lines;
identifying the information of the inserted characters according to the average height of the text lines;
extracting image features of the text lines by utilizing a pre-constructed multilayer convolutional neural network model;
extracting character sequence features from the image features by utilizing a pre-constructed bidirectional recurrent neural network model;
and identifying the text content of the image to be identified according to the inserted character information and the character sequence characteristics.
Further, the method further comprises:
removing Gaussian noise of the image to be identified in the scanning process by using a Gaussian filtering technology;
and removing long straight lines appearing in the image to be recognized by adopting a straight line detection algorithm.
Further, segmenting the image to be recognized into text lines includes:
segmenting the examinee answer image by a line unit through image transverse projection and a water drop algorithm;
performing longitudinal projection segmentation on the writing inclined image of the line with the height exceeding the average height, and removing redundant blanks on the upper side and the lower side to obtain a text line;
all text lines are integrated in sequence into an image sequence.
Further, the identifying the position of the inserted character according to the average height of the text line includes:
calculating the line average height of all text lines;
screening out a target text line with the height exceeding the average line height;
searching a region of the target text line, in which the inserted characters possibly exist, through text projection, and taking the region as a candidate region of interest;
extracting haar features of the candidate interesting regions, and clustering the candidate interesting regions according to the haar features of the candidate interesting regions;
inputting the clustering result into a support vector machine classifier, and screening out an interested region;
and identifying the inserted character and the position of the inserted character in the region of interest by utilizing a character recognition technology.
Further, the method for constructing the multilayer convolutional neural network model comprises the following steps:
setting the number of the convolution layers as six layers;
setting the convolution kernel size to 3 x 3;
selecting a maximum pooling layer;
adding batch normalization layers to the fourth convolution layer and the fifth convolution layer;
and collecting historical text lines, constructing a first training set by using the historical text lines, and training the multilayer convolutional neural network model by using the first training set.
Further, the method for constructing the bidirectional recurrent neural network model comprises the following steps:
constructing a bidirectional cyclic neural network model;
collecting historical image features as a second training set;
introducing null characters to non-character positions in the historical image features of the second training set;
and training the bidirectional recurrent neural network model by using a time sequence classification algorithm and the second training set.
Further, the recognizing the text content of the image to be recognized according to the inserted character information and the character sequence features includes:
according to the inserted character information and the character sequence characteristics, recognizing the content of the text line by utilizing a character recognition technology, and integrating the content of the text line into the text content of the image to be recognized;
and removing the scratch-out characters and the spaces in the text content, and outputting a final recognition result.
In a second aspect, the present invention provides a Chinese handwriting recognition system based on text line detection, comprising:
the image segmentation unit is configured to segment the image to be identified into text lines;
the symbol recognition unit is configured for recognizing the inserted character information according to the average height of the text line;
the characteristic identification unit is configured to extract the image characteristics of the text line by utilizing a pre-constructed multilayer convolutional neural network model;
the sequence recognition unit is configured to extract character sequence features from the image features by utilizing a pre-constructed bidirectional recurrent neural network model;
and the content identification unit is configured to identify the text content of the image to be identified according to the inserted character information and the character sequence characteristics.
Further, the image segmentation unit includes:
the segmentation execution module is configured for segmenting the examinee answer image in a row unit through image transverse projection and a water drop algorithm;
the blank removing module is configured for longitudinally projecting and segmenting the writing inclined image of the line with the height exceeding the average height, and removing redundant blanks on the upper side and the lower side to obtain a text line;
and the sequence integration module is configured for sequentially integrating all text lines into an image sequence.
Further, the symbol recognition unit includes:
a height calculation module configured to calculate a line average height of all text lines;
the height comparison module is configured to screen out target text lines with heights exceeding the line average height;
the projection searching module is configured to search a region of the target text line, where the inserted character may exist, through text projection, and take the region as a candidate region of interest;
the characteristic clustering module is configured for extracting the haar characteristics of the candidate interesting regions and clustering the candidate interesting regions according to the haar characteristics of the candidate interesting regions;
the clustering screening module is configured for inputting the clustering result into the support vector machine classifier and screening out the region of interest;
and the insertion recognition module is configured for recognizing the insertion characters and the insertion character positions of the region of interest by utilizing a character recognition technology.
The beneficial effect of the invention is that,
the Chinese handwriting recognition method and system based on text line detection can accurately recognize text contents in handwritten Chinese images and have high recognition efficiency.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic flow diagram of the insertion character recognition of a method of one embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
The following explains key terms appearing in the present invention.
Specifically, referring to fig. 1, the method for recognizing chinese handwriting based on text line detection includes:
step (1): and preprocessing the answer image of the examinee.
And preprocessing the examinee answer image, wherein Gaussian filtering is adopted in the preprocessing process to remove Gaussian noise of the image in the scanning process, and a straight line detection algorithm is adopted to remove long straight lines such as text boxes appearing in the partial image, so that the whole image I is obtained.
Step (2): and cutting the preprocessed image and removing the upper blank area and the lower blank area.
Segmenting the examinee answer image through image transverse projection and water drop algorithm to obtain a segmented image I i Of size h i ×w i . Then, longitudinally projecting and cutting the writing inclined image with the height of the cut image far larger than the average height Th, removing redundant blanks on the upper side and the lower side, and then merging and cutting the images again to obtain a line cut image I j Of size h j ×w i Finally obtaining nxh j ×w i The lines of (a) are cut into the image sequence. Finally, the n line segmentation image sequences are converted into n multiplied by h multiplied by w with fixed height i Image sequence I n 。
And (3): as shown in fig. 2, the text line image suspected of having an caret is placed into an caret processing module for caret recognition and processing.
For line-slicing image sequences I n Since the height of the image in which the inserted character exists is much larger than the average height of the n line-segmented images, the method detects the inserted character in the line-segmented image in which the inserted character is suspected to exist. In order to reduce the number of the interested regions and combine with actual conditions, through projection, a region where the inserted characters possibly exist is found to be used as a candidate interested region, haar features are extracted from all candidate regions, after clustering, an SVM classifier is input, and candidate region images are screened. If the inserting symbol exists, the image above or below the inserting symbol is transmitted into a character recognition module, and a recognition result is output.
And (4): and extracting image features by adopting a multilayer convolutional neural network.
The invention uses 6 layers of convolution neural networks, the convolution kernels are all 3 × 3 small convolution kernels, the maximum pooling is selected for the pooling layer after the convolution layers, and the BN layer is added after the pooling layers of the 4 th layer and the 5 th layer, thereby accelerating the training and convergence speed of the network and playing the role of preventing overfitting.
This step yields a feature data matrix X = [ X ] for a 4-dimensional image 1 ,x 2 ,...,x n ]∈R n×d And the width, the height, the depth and the like of the feature matrix are consistent.
And (5): and introducing the extracted image features into a bidirectional recurrent neural network to continuously extract character sequence features.
The circulating network layer in the invention is a two-layer bidirectional LSTM network, and character sequence characteristics are continuously extracted on the basis of convolution characteristics. And (4) transmitting the characteristic data matrix obtained in the step (4) into an LSTM network, and extracting the sequence characteristics of the characters.
And (6): and processing the recognition result into blank spaces, wrongly written characters, inserted characters and the like, and adjusting the corresponding sequence.
After the network recognition is finished, post-processing of the recognition result is carried out, namely, characters and spaces in the recognition result are removed and replaced, so that the semantics of the recognition result are not influenced.
The embodiment provides a Chinese handwriting recognition system based on text line detection, which comprises:
the image segmentation unit is configured to segment the image to be identified into text lines;
the symbol recognition unit is configured for recognizing the inserted character information according to the average height of the text line;
the characteristic identification unit is configured for extracting the image characteristics of the text line by utilizing a pre-constructed multilayer convolutional neural network model;
the sequence recognition unit is configured for extracting character sequence features from the image features by utilizing a pre-constructed bidirectional recurrent neural network model;
and the content identification unit is configured to identify the text content of the image to be identified according to the inserted character information and the character sequence characteristics.
Optionally, as an embodiment of the present invention, the image segmentation unit includes:
the segmentation execution module is configured for segmenting the examinee answer image in a row unit through image transverse projection and a water drop algorithm;
the blank removing module is configured for longitudinally projecting and segmenting the writing inclined image of the line with the height exceeding the average height, and removing redundant blanks on the upper side and the lower side to obtain a text line;
and the sequence integration module is configured for sequentially integrating all text lines into an image sequence.
Optionally, as an embodiment of the present invention, the symbol recognition unit includes:
the height calculation module is configured for calculating the line average height of all text lines;
the height comparison module is configured to screen out target text lines with heights exceeding the line average height;
the projection searching module is configured to search a region of the target text line, where the inserted character may exist, through text projection, and take the region as a candidate region of interest;
the characteristic clustering module is configured for extracting the haar characteristics of the candidate interesting regions and clustering the candidate interesting regions according to the haar characteristics of the candidate interesting regions;
the clustering screening module is configured for inputting the clustering result into the support vector machine classifier and screening out the region of interest;
and the insertion recognition module is configured for recognizing the insertion character and the insertion character position of the region of interest by utilizing a character recognition technology.
Although the present invention has been described in detail in connection with the preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A Chinese handwriting recognition method based on text line detection is characterized by comprising the following steps:
segmenting an image to be identified into text lines;
identifying the information of the inserted characters according to the average height of the text lines;
extracting image features of the text lines by utilizing a pre-constructed multilayer convolutional neural network model;
extracting character sequence features from the image features by using a pre-constructed bidirectional recurrent neural network model
Performing identification;
recognizing the text content of the image to be recognized according to the inserted character information and the character sequence characteristics;
the identifying of the position of the inserted character according to the average height of the text line comprises the following steps:
calculating the average height of all text lines;
screening out target text lines with the height exceeding the line average height;
searching a region of the target text line, in which the inserted characters possibly exist, through text projection, and taking the region as a candidate region of interest;
extracting haar features of the candidate interesting regions, and clustering the candidate interesting regions according to the haar features of the candidate interesting regions;
inputting the clustering result into a support vector machine classifier, and screening out an interested region;
and identifying the inserted character and the position of the inserted character in the region of interest by utilizing a character recognition technology.
2. The method of claim 1, further comprising:
removing Gaussian noise of the image to be identified in the scanning process by utilizing a Gaussian filtering technology;
and removing long straight lines appearing in the image to be recognized by adopting a straight line detection algorithm.
3. The method according to claim 1, wherein the segmenting the image to be recognized into text lines comprises:
segmenting the examinee answer image by a line unit through image transverse projection and a water drop algorithm;
longitudinally projecting and segmenting the writing inclined image of the line with the height exceeding the average height, and removing redundant blanks on the upper side and the lower side to obtain a text line;
all text lines are integrated in sequence into an image sequence.
4. The method of claim 1, wherein the method for constructing the multilayer convolutional neural network model comprises:
setting the number of the convolution layers as six layers;
setting the convolution kernel size to 3 x 3;
selecting a maximum pooling layer;
adding batch normalization layers to the fourth convolution layer and the fifth convolution layer;
and collecting historical text lines, constructing a first training set by using the historical text lines, and training the multilayer convolutional neural network model by using the first training set.
5. The method of claim 1, wherein the method for constructing the bidirectional recurrent neural network model comprises:
constructing a bidirectional cyclic neural network model;
collecting historical image features as a second training set;
introducing null characters to non-character positions in the historical image features of the second training set;
and training the bidirectional recurrent neural network model by using a time sequence classification algorithm and the second training set.
6. The method of claim 1, wherein the recognizing the text content of the image to be recognized according to the inserted character information and the character sequence features comprises:
according to the inserted character information and the character sequence characteristics, recognizing the content of the text line by utilizing a character recognition technology, and integrating the content of the text line into the text content of the image to be recognized;
and removing the scratch-out characters and the spaces in the text content, and outputting a final recognition result.
7. A chinese handwriting recognition system based on text line detection, comprising:
the image segmentation unit is configured to segment the image to be identified into text lines;
the symbol recognition unit is configured for recognizing the inserted character information according to the average height of the text line;
the characteristic identification unit is configured to extract the image characteristics of the text line by utilizing a pre-constructed multilayer convolutional neural network model;
the sequence recognition unit is configured to extract character sequence features from the image features by utilizing a pre-constructed bidirectional recurrent neural network model;
the content identification unit is configured to identify the text content of the image to be identified according to the inserted character information and the character sequence characteristics;
the symbol recognition unit includes:
a height calculation module configured to calculate a line average height of all text lines;
the height comparison module is configured for screening out a target text line with the height exceeding the average line height;
the projection searching module is configured to search a region of the target text line, where the inserted character may exist, through text projection, and take the region as a candidate region of interest;
the characteristic clustering module is configured for extracting the haar characteristics of the candidate interesting regions and clustering the candidate interesting regions according to the haar characteristics of the candidate interesting regions;
the cluster screening module is configured for inputting a cluster result into the support vector machine classifier and screening out an interested region;
and the insertion recognition module is configured for recognizing the insertion character and the insertion character position of the region of interest by utilizing a character recognition technology.
8. The system of claim 7, wherein the image segmentation unit comprises:
the segmentation execution module is configured for segmenting the examinee answer image in a row unit through image transverse projection and a water drop algorithm;
the blank removing module is configured for longitudinally projecting and segmenting the writing inclined image of the line with the height exceeding the average height, and removing redundant blanks on the upper side and the lower side to obtain a text line;
and the sequence integration module is configured for sequentially integrating all text lines into an image sequence.
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| CN115862045B (en) * | 2023-02-16 | 2023-05-26 | 中国人民解放军总医院第一医学中心 | Case automatic identification method, system, equipment and storage medium based on image-text identification technology |
| CN117894033B (en) * | 2024-03-14 | 2024-05-28 | 山东山大鸥玛软件股份有限公司 | Method and system for checking consistency of answer sheets based on OCR (optical character recognition) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108351745A (en) * | 2015-08-25 | 2018-07-31 | 迈思慧公司 | The system and method for digital notes record |
| CN110287952A (en) * | 2019-07-01 | 2019-09-27 | 中科软科技股份有限公司 | A kind of recognition methods and system for tieing up sonagram piece character |
| CN111091124A (en) * | 2019-12-04 | 2020-05-01 | 吉林大学 | Spine character recognition method |
| CN111222589A (en) * | 2018-11-27 | 2020-06-02 | 中国移动通信集团辽宁有限公司 | Image text recognition method, device, equipment and computer storage medium |
| CN111368632A (en) * | 2019-12-27 | 2020-07-03 | 上海眼控科技股份有限公司 | Signature identification method and device |
| CN111652332A (en) * | 2020-06-09 | 2020-09-11 | 山东大学 | Deep learning handwritten Chinese character recognition method and system based on two classifications |
| CN112686345A (en) * | 2020-12-31 | 2021-04-20 | 江南大学 | Off-line English handwriting recognition method based on attention mechanism |
| CN112990175A (en) * | 2021-04-01 | 2021-06-18 | 深圳思谋信息科技有限公司 | Method and device for recognizing handwritten Chinese characters, computer equipment and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101183283A (en) * | 2007-12-04 | 2008-05-21 | 哈尔滨工业大学深圳研究生院 | Method for directly writing handwriting information |
| CN108509881A (en) * | 2018-03-22 | 2018-09-07 | 五邑大学 | A kind of the Off-line Handwritten Chinese text recognition method of no cutting |
| CN110298343A (en) * | 2019-07-02 | 2019-10-01 | 哈尔滨理工大学 | A kind of hand-written blackboard writing on the blackboard recognition methods |
| CN111008594B (en) * | 2019-12-04 | 2024-04-19 | 科大讯飞股份有限公司 | Error-correction question review method, related device and readable storage medium |
-
2020
- 2020-12-22 CN CN202011526382.3A patent/CN112651323B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108351745A (en) * | 2015-08-25 | 2018-07-31 | 迈思慧公司 | The system and method for digital notes record |
| CN111222589A (en) * | 2018-11-27 | 2020-06-02 | 中国移动通信集团辽宁有限公司 | Image text recognition method, device, equipment and computer storage medium |
| CN110287952A (en) * | 2019-07-01 | 2019-09-27 | 中科软科技股份有限公司 | A kind of recognition methods and system for tieing up sonagram piece character |
| CN111091124A (en) * | 2019-12-04 | 2020-05-01 | 吉林大学 | Spine character recognition method |
| CN111368632A (en) * | 2019-12-27 | 2020-07-03 | 上海眼控科技股份有限公司 | Signature identification method and device |
| CN111652332A (en) * | 2020-06-09 | 2020-09-11 | 山东大学 | Deep learning handwritten Chinese character recognition method and system based on two classifications |
| CN112686345A (en) * | 2020-12-31 | 2021-04-20 | 江南大学 | Off-line English handwriting recognition method based on attention mechanism |
| CN112990175A (en) * | 2021-04-01 | 2021-06-18 | 深圳思谋信息科技有限公司 | Method and device for recognizing handwritten Chinese characters, computer equipment and storage medium |
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